摘要：
Sensor networks, as a promising network paradigm, have been widely applied in a great deal of critical real-world applications. A key challenge in sensor networks is how to improve and optimize coverage quality which is a fundamental metric to characterize how well a point or a region or a barrier can be sensed by the geographically deployed heterogeneous sensors. Because of the resource-limited, battery-powered and type-diverse features of the sensors, maintaining and optimizing coverage quality includes a significant amount of challenges in heterogeneous sensor networks. Many researchers from both academic and industrial communities have performed numerous significant works on coverage optimization problem in the past decades. Some of them also have surveyed the current models, theories and solutions on the problem of coverage optimization. However, most of the existing surveys and analytical studies ignore how to exploit data fusion and cooperation of the deployed sensors to enhance coverage performance. In this paper, we provide an insightful and comprehensive summarization and classification on the data fusion based coverage optimization problem and techniques. Aiming at overcoming the shortcomings existed in current solutions, we also discuss the future issues and challenges in this area and sketch a general research framework in the context of reinforcement learning.
期刊：
IEEE INTERNET OF THINGS JOURNAL,2019年6(6):9217-9225 ISSN：2327-4662
通讯作者：
Deng, Xianjun
作者机构：
[Dai, Lu; Wang, Bang] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Hubei, Peoples R China.;[Yang, Laurence T.] St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS B2G2W5, Canada.;[Deng, Xianjun] Univ South China, Sch Elect Engn, Hunan Prov Key Lab Ultra Fast Micro Nano Technol, Hengyang 421001, Peoples R China.;[Yi, Lingzhi] Univ South China, Sch Civil Engn, Hengyang 421001, Peoples R China.
通讯机构：
[Deng, Xianjun] U;Univ South China, Sch Elect Engn, Hunan Prov Key Lab Ultra Fast Micro Nano Technol, Hengyang 421001, Peoples R China.
关键词：
Confident information coverage (CIC);genetic algorithms (GEAs);Industrial Internet of Things (IIoT);Internet of Things (IoT) node deployment;network lifetime
摘要：
The ever-growing Industrial Internet of Things (IoT) provides a powerful method to sense a series of critical industrial environments. This paper studies how to deploy the fixed number of IoT nodes so that the network lifetime is maximized in a sensing field with obstacles while guaranteeing the requirements of confident information coverage, network connectivity, energy efficiency, fault tolerance, and reliability. An IoT node deployment scheme based on an improved nature-inspired genetic algorithm is proposed to solve the defined constrained optimization problem. In the proposed IoT node deployment scheme, we utilize a population initialization based on the Delaunay triangulation to generate the better initial population, a chromosome modification operation to achieve both connectivity and coverage for each chromosome and a chromosome mirror-crossover operation to produce the better offsprings. Experimental results show that our deployment schema equips better performance in terms of longer network lifetime and comparable coverage ratio compared with the other four peer algorithms.
摘要：
针对具有较大多普勒扩展和时延扩展的车载通信环境,利用后训练序列信道响应携带的信道变化信息,提出一种结合后训练序列的判决反馈信道估计方法。该方法采用最小二乘算法估计后训练序列的信道响应;对前一个正交频分复用(orthogonal frequency division multiplexing, OFDM)符号和后训练序列的信道响应估计值进行系数加权求和来估计当前OFDM符号的信道响应,并利用其4个导频子载波的信道频率响应关系自动获取加权系数;最后,对获得的信道响应估计值进行判决反馈和低通滤波以降低噪声影响。仿真结果表明,与目前取得较好性能的STA(spectral temporal averaging)方法、CDP(constructing data pilot)方法和结合平滑滤波的判决反馈信道估计方法相比,所提方法具有更优的误包率性能。
关键词：
Barrier coverage;Internet of Things (IoT);barrier gap;directional sensor networks;line-based deployment
摘要：
The barrier coverage of a wireless sensor network is an important surveillance application of Internet of Things. Barrier coverage guarantees that all intruders traversing the protected region are detected by a chain of connected sensors. However, when the sensors are randomly deployed, barrier gaps may occur due to deployment randomness or insufficient sensors. How to locate the barrier gaps and mend them is an important aspect in the network. In this paper, we study the barrier gap problem in weak barrier coverage and strong barrier coverage that consist of directional sensors, and the sensors are deployed by a line-based deployment strategy. A gap-finding algorithm is proposed to find subbarriers and barrier gaps. Two gap-mending algorithms are devised to mend barrier gaps in the network: One algorithm is a simple rotation algorithm that only rotates two critical sensors in two subbarriers to fix the gap, and the other algorithm is a chain-reaction rotation algorithm that rotates sensors in the subbarrier in a chain-reaction manner to mend the gap. We conduct extensive simulations to evaluate the performance of the proposed algorithms. Simulation results show that the proposed gap-mending algorithms can effectively fix barrier gaps and improve the probability of barrier success construction.
摘要：
针对多用户配对虚拟MIMO (multiple input multiple output)安全性差,对信道估计器依赖性强的问题,提出一种基于非相干空频码(non-coherent space frequency code, NSFC)和正交频分复用(orthogonal frequency division multiplexing, OFDM)的非协作式虚拟MIMO。在分析NSFC成对错误概率的基础上,给出能满足全分集阶数和最大编码增益的编码准则。为了获得平行子信道传输效果,利用信道循环矩阵奇异值分解(singular value decomposition, SVD)后得到的酉矩阵分别进行预编码和预解码。基于最优NSFC和OFDM预编解码,提出一种新颖的虚拟MIMO策略。该虚拟MIMO在收发两端均无需信道瞬时信息,以非协作方式在单天线内模拟多天线收发效果。理论和仿真分析结果表明,虚拟MIMO系统能有效逼近实际理想MIMO的系统容量和误比特率性能,显著降低了虚拟MIMO系统的检测门限。
